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Markus Geipel and Michael Beetz. Learning to shoot goals, Analysing the Learning Process and the Resulting Policies. In RoboCup-2006: Robot Soccer World Cup X, Springer Verlag, Berlin, 2006. to be published
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Abstract
Reinforcement learning is a very general unsupervised learning mechanism. Due to its generality reinforcement learning does not scale very well for tasks that involve inferring subtasks. In particular when the subtasks are dynamically changing and the environment is adversarial. One of the most challenging reinforcement learning tasks so far has been the 3 to 2 keepaway task in the RoboCup simulation league. In this paper we apply reinforcement learning to a even more challenging task: attacking the opponents goal. The main contribution of this paper is the empirical analysis of a portfolio of mechanisms for scaling reinforcement learning towards learning attack policies in simulated robot soccer.
BibTeX Entry
@InProceedings{geipel06learning,
author = {Markus Geipel and Michael Beetz},
title = {Learning to shoot goals, Analysing the Learning
Process and the Resulting Policies},
editor = {Gerhard Lakemeyer and Elizabeth Sklar and Domenico
Sorenti and Tomoichi Takahashi},
note = {to be published},
year = {2006},
booktitle = {RoboCup-2006: Robot Soccer World Cup X},
organization = {RoboCup},
publisher = {Springer Verlag, Berlin},
bib2html_pubtype ={Refereed Conference Paper},
bib2html_rescat = {Robocup},
bib2html_groups = {IAS},
abstract = {Reinforcement learning is a very general
unsupervised learning mechanism. Due to its
generality reinforcement learning does not scale
very well for tasks that involve inferring
subtasks. In particular when the subtasks are
dynamically changing and the environment is
adversarial. One of the most challenging
reinforcement learning tasks so far has been the 3
to 2 keepaway task in the RoboCup simulation
league. In this paper we apply reinforcement
learning to a even more challenging task: attacking
the opponents goal. The main contribution of this
paper is the empirical analysis of a portfolio of
mechanisms for scaling reinforcement learning
towards learning attack policies in simulated robot
soccer.}
}
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